Surface roughness modeling

• Main Authors: PATRIKAR, Rajendra M., RAMANATHAN, Kiruthika
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2002
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7433
• Institution: Singapore Management University

Microelectronic and molecular devices are formed on the surfaces, which are microscopically rough. To understand how the devices are formed on the rough surfaces and to model their electrical behavior surface modeling has become essential. In this work CAD tool to generate surfaces with roughness has been developed. To represent the surface we have implemented Fast Fourier Transform (FFT), Mandelbrot Weierstrass function, and backpropagation neural networks. FFT method was used because it has been used traditionally for surface modeling. We used the concept of self-similar fractals to model the rough surface (M-W function) because it has been shown that the fractal dimension (D) can quantitatively describe surface microscopic roughness and it is scale independent. We are using Neural Networks to model these surfaces to map the process parameters to roughness parameters.